Automatic Generation of Board Game Manuals

We present initial research towards procedural generation of Simplified Boardgames and translating them into an efficient GDL code. This is a step towards establishing Simplified Boardgames as a comparison class for General Game Playing agents. To generate playable, human readable, and balanced chess-like games we use an adaptive evolutionary algorithm with the fitness function based on simulated playouts. In future, we plan to use the proposed method to diversify and extend the set of GGP tournament games by those with fully automatically generated rules.

2019 IEEE Conference on Games (CoG), 2019

Although General Game Playing (GGP) systems can facilitate useful research in Artificial Intelligence (AI) for gameplaying, they are often computationally inefficient and somewhat specialised to a specific class of games. However, since the start of this year, two General Game Systems have emerged that provide efficient alternatives to the academic state of the artthe Game Description Language (GDL). In order of publication, these are the Regular Boardgames language (RBG), and the Ludii system. This paper offers an experimental evaluation of Ludii. Here, we focus mainly on a comparison between the two new systems in terms of two key properties for any GGP system: simplicity/clarity (e.g. human-readability), and efficiency. Index Terms-General Game Playing, knowledge representation, General Game AI.

2019

Ludii is a new general game system, currently under development, which aims to support a wider range of games than existing systems and approaches. It is being developed primarily for the task of game design, but o↵ers a number of other potential benefits for game and AI researchers, professionals and hobbyists. This paper is based on an interactive demonstration of Ludii at thuis year’s Advances in Computer Games conference (ACG 2019). It describes the approach behind Ludii, how it works, how it is used, and what it can potentially do.

2020

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently prop...

arXiv (Cornell University), 2022

There are several different game description languages (GDLs), each intended to allow wide ranges of arbitrary games (i.e., general games) to be described in a single higher-level language than general-purpose programming languages. Games described in such formats can subsequently be presented as challenges for automated general game playing agents, which are expected to be capable of playing any arbitrary game described in such a language without prior knowledge about the games to be played. The language used by the Ludii general game system was previously shown to be capable of representing equivalent games for any arbitrary, finite, deterministic, fully observable extensive-form game. In this paper, we prove its universality by extending this to include finite nondeterministic and imperfect-information games.

2009

Interactive simulation games used for training usually require a large amount of coherent narrative content. An effective and efficient solution to the narrative content creation problem is to use Natural Language Generation (NLG) systems. The use of NLG systems, however, requires sophisticated linguistic and sometimes programming knowledge. For this reason, NLG systems are typically not accessible to the game designers who write narrative content. We have designed and implemented a visual environment for creating and modifying NLG templates that requires no programming knowledge, and can operate with a minimum of linguistic knowledge. It allows specifying templates with any number of variables and dependencies between them. It automatically generates all the sentences that follow the created template. It uses SimpleNLG to provide the linguistic background knowledge. We tested the performance of our system in the context of an interactive simulation game.

2021 IEEE Conference on Games (CoG), 2021

Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of "game concept", inspired by terms generally used by game players and designers. Through the Ludii General Game System, we describe concepts for several levels of abstraction, such as the game itself, the moves played, or the states reached. This new GGP feature associated with the ludeme representation of games opens many new lines of research. The creation of a hyper-agent selector, the transfer of AI learning between games, or explaining AI techniques using game terms, can all be facilitated by the use of game concepts. Other applications which can benefit from game concepts are also discussed, such as the generation of plausible reconstructed rules for incomplete ancient games, or the implementation of a board game recommender system.

Proceedings of the 2009 Workshop on Language Generation and Summarisation - UCNLG+Sum '09, 2009

Users of Natural Language Generation systems are required to have sophisticated linguistic and sometimes even programming knowledge, which has hindered the adoption of this technology by individuals outside the computational linguistics research community. We have designed and implemented a visual environment for creating and modifying NLG templates which requires no programming ability and minimum linguistic knowledge. It allows specifying templates with any number of variables and dependencies between them. Internally, it uses SimpleNLG to provide the linguistic background knowledge. We tested the performance of our system in the context of an interactive simulation game. We describe the templates used for testing and show examples of sentences that our system generates from these templates.

We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for com- putationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar G cardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold ’em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammar- guided genetic program.

HAL (Le Centre pour la Communication Scientifique Directe), 2020

This document provides full documentation for the game description language used by the Ludii general game system. Note that the majority of this document is automatically generated.